from collections.abc import AsyncGenerator from pathlib import Path import gradio as gr import httpx from httpx_sse import aconnect_sse from openai import AsyncOpenAI from faster_whisper_server.config import Config, Task from faster_whisper_server.hf_utils import PiperModel # FIX: this won't work on ARM from faster_whisper_server.routers.speech import ( DEFAULT_VOICE, MAX_SAMPLE_RATE, MIN_SAMPLE_RATE, SUPPORTED_RESPONSE_FORMATS, ) TRANSCRIPTION_ENDPOINT = "/v1/audio/transcriptions" TRANSLATION_ENDPOINT = "/v1/audio/translations" TIMEOUT_SECONDS = 180 TIMEOUT = httpx.Timeout(timeout=TIMEOUT_SECONDS) def create_gradio_demo(config: Config) -> gr.Blocks: # noqa: C901, PLR0915 base_url = f"http://{config.host}:{config.port}" # TODO: test that auth works http_client = httpx.AsyncClient( base_url=base_url, timeout=TIMEOUT, headers={"Authorization": f"Bearer {config.api_key}"} if config.api_key else {}, ) openai_client = AsyncOpenAI( base_url=f"{base_url}/v1", api_key=config.api_key if config.api_key else "cant-be-empty" ) async def whisper_handler( file_path: str, model: str, task: Task, temperature: float, stream: bool ) -> AsyncGenerator[str, None]: if task == Task.TRANSCRIBE: endpoint = TRANSCRIPTION_ENDPOINT elif task == Task.TRANSLATE: endpoint = TRANSLATION_ENDPOINT if stream: previous_transcription = "" async for transcription in streaming_audio_task(file_path, endpoint, temperature, model): previous_transcription += transcription yield previous_transcription else: yield await audio_task(file_path, endpoint, temperature, model) async def audio_task(file_path: str, endpoint: str, temperature: float, model: str) -> str: with Path(file_path).open("rb") as file: # noqa: ASYNC230 response = await http_client.post( endpoint, files={"file": file}, data={ "model": model, "response_format": "text", "temperature": temperature, }, ) response.raise_for_status() return response.text async def streaming_audio_task( file_path: str, endpoint: str, temperature: float, model: str ) -> AsyncGenerator[str, None]: with Path(file_path).open("rb") as file: # noqa: ASYNC230 kwargs = { "files": {"file": file}, "data": { "response_format": "text", "temperature": temperature, "model": model, "stream": True, }, } async with aconnect_sse(http_client, "POST", endpoint, **kwargs) as event_source: async for event in event_source.aiter_sse(): yield event.data async def update_whisper_model_dropdown() -> gr.Dropdown: models = (await openai_client.models.list()).data model_names: list[str] = [model.id for model in models] assert config.whisper.model in model_names recommended_models = {model for model in model_names if model.startswith("Systran")} other_models = [model for model in model_names if model not in recommended_models] model_names = list(recommended_models) + other_models return gr.Dropdown( choices=model_names, label="Model", value=config.whisper.model, ) async def update_piper_voices_dropdown() -> gr.Dropdown: res = (await http_client.get("/v1/audio/speech/voices")).raise_for_status() piper_models = [PiperModel.model_validate(x) for x in res.json()] return gr.Dropdown(choices=[model.voice for model in piper_models], label="Voice", value=DEFAULT_VOICE) async def handle_audio_speech( text: str, voice: str, response_format: str, speed: float, sample_rate: int | None ) -> Path: res = await openai_client.audio.speech.create( input=text, model="piper", voice=voice, # pyright: ignore[reportArgumentType] response_format=response_format, # pyright: ignore[reportArgumentType] speed=speed, extra_body={"sample_rate": sample_rate}, ) audio_bytes = res.response.read() file_path = Path(f"audio.{response_format}") with file_path.open("wb") as file: # noqa: ASYNC230 file.write(audio_bytes) return file_path with gr.Blocks(title="faster-whisper-server Playground") as demo: gr.Markdown( "### Consider supporting the project by starring the [repository on GitHub](https://github.com/fedirz/faster-whisper-server)." ) with gr.Tab(label="Transcribe/Translate"): audio = gr.Audio(type="filepath") model_dropdown = gr.Dropdown( choices=[config.whisper.model], label="Model", value=config.whisper.model, ) task_dropdown = gr.Dropdown( choices=[task.value for task in Task], label="Task", value=Task.TRANSCRIBE, ) temperature_slider = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label="Temperature", value=0.0) stream_checkbox = gr.Checkbox(label="Stream", value=True) button = gr.Button("Generate") output = gr.Textbox() # NOTE: the inputs order must match the `whisper_handler` signature button.click( whisper_handler, [audio, model_dropdown, task_dropdown, temperature_slider, stream_checkbox], output ) with gr.Tab(label="Speech Generation"): # TODO: add warning about ARM text = gr.Textbox(label="Input Text") voice_dropdown = gr.Dropdown( choices=["en_US-amy-medium"], label="Voice", value="en_US-amy-medium", info=""" The last part of the voice name is the quality (x_low, low, medium, high). Each quality has a different default sample rate: - x_low: 16000 Hz - low: 16000 Hz - medium: 22050 Hz - high: 22050 Hz """, ) response_fromat_dropdown = gr.Dropdown( choices=SUPPORTED_RESPONSE_FORMATS, label="Response Format", value="wav", ) speed_slider = gr.Slider(minimum=0.25, maximum=4.0, step=0.05, label="Speed", value=1.0) sample_rate_slider = gr.Number( minimum=MIN_SAMPLE_RATE, maximum=MAX_SAMPLE_RATE, label="Desired Sample Rate", info=""" Setting this will resample the generated audio to the desired sample rate. You may want to set this if you are going to use voices of different qualities but want to keep the same sample rate. Default: None (No resampling) """, value=lambda: None, ) button = gr.Button("Generate Speech") output = gr.Audio(type="filepath") button.click( handle_audio_speech, [text, voice_dropdown, response_fromat_dropdown, speed_slider, sample_rate_slider], output, ) demo.load(update_whisper_model_dropdown, inputs=None, outputs=model_dropdown) demo.load(update_piper_voices_dropdown, inputs=None, outputs=voice_dropdown) return demo